Ball screw fault diagnosis based on continuous wavelet transform and two-dimensional convolution neural network

Yu, Di; Zhan, Changshu; Zhao, Qiancheng; Wang, Junxiang; Liu, Jiuqing; Liu, Jiaxiu

doi:10.1177/00202940221107620

Cited by 15 publications

(9 citation statements)

References 14 publications

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“…In this paper, the wavelet transform of the ordered point filtering is used as the noise reduction method of system error. Wavelet transform is a new numerical analysis method based on the short-time Fourier transform (STFT) [17]:…”

Section: Denoise Methods Based On Wavelet Transformationmentioning

confidence: 99%

Measurement and reconstruction of geometric parameters of the barrel bore based on the laser scanning strategy

Zhang

Jin²,

Ren³

et al. 2023

Front. Phys.

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The inner surface defects can be displayed intuitively by measuring the geometric parameters of rifling of the artillery barrel. In this paper, the parameters of the barrel bore were scanned based on the high-precision laser, and the three-dimensional reconstruction of the bore shape was conducted based on the test data. The wavelet transform was used for multiple de-noising of the test data, and the Delaunay triangulation interpolation algorithm was used to reconstruct the three-dimensional contour structure of the barrel bore, forming a high-fidelity measurement strategy for the parameters of the barrel bore. The results show that this measurement strategy can achieve the high-precision measurement of the geometric parameters of barrel rifling, and the accuracy can reach .001 mm. By comparing the measured value of rifling with the standard value, the flaw points in the rifling of the birth tube can be accurately specified. The three-dimensional model reconstruction based on the massive sample data realizes the high-fidelity measurement of rifling geometric parameters. This measurement strategy can provide support for the visualization of barrel rifling and effectively improve the detection accuracy of the barrel bore.

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Section: Denoise Methods Based On Wavelet Transformationmentioning

confidence: 99%

Measurement and reconstruction of geometric parameters of the barrel bore based on the laser scanning strategy

Zhang

Jin²,

Ren³

et al. 2023

Front. Phys.

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“…The CWT is an adaptive method for time-frequency analysis, and the resolution in both frequency and time domains can be effectively balanced [27,28]. The wavelet basis function ψ a,τ (t) is given as [27,28],…”

Section: Continuous Wavelet Transform (Cwt)mentioning

confidence: 99%

“…where a represents the scaling parameter, ψ (t) is mother wavelet, and the commonly employed wavelet functions including the Morlet wavelet, Gabor wavelet, Haar wavelet and others. However, Morlet wavelet function exhibits favorable temporal localization characteristics, which is denoted as [27,28],…”

Section: Continuous Wavelet Transform (Cwt)mentioning

confidence: 99%

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Fault diagnosis of rotating machinery using novel self-attention mechanism TCN with soft thresholding method

Ding,

2024

Meas. Sci. Technol.

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Rotating machinery (e.g., rolling bearing and gearbox) are usually operated in high-risk and vulnerable environments such as time-varying loads and poor lubrication. Timely assessment of the operational status for rotating machinery is crucial to prevent damage caused by potential failure and shutdown, which significantly enhances the reliability of mechanical systems, prolongs the service life of critical components in rotating machinery, and minimizes unnecessary maintenance costs. To this regard, in this paper, a novel approach named self-attention mechanism combined time convolutional network with soft thresholding algorithm (SAM-TCN-ST) is proposed for fault intelligent recognition of rotating machinery. Specifically, the vibration signals are transformed into time-frequency graphs with distinct features utilizing the continuous wavelet transform (CWT), and then the proposed SAM-TCN-ST algorithm is employed for capturing essential data characteristics and classification performance. Eventually, the datasets from rolling bearings and gearbox are used for verifying the accuracy and effectiveness of the proposed method compared with state-of-the-art benchmark networks such as pure TCN, convolutional neural network (CNN) and long short-term memory (LSTM) models. Experimental results demonstrate that the recognition accuracy rate of the proposed SAM-TCN-ST is higher than that obtained from the benchmark methods. This research presents an intelligent and viable solution for achieving real-time monitoring of the status and detecting faults in rotating machinery, thereby expectedly enhancing the reliability of mechanical systems. Consequently, the proposed SAM-TCN-ST algorithm holds significant potential for application in prognostic and health management (PHM) practices related to rotating machinery.

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“…In recent years, various intelligent fault diagnosis methods combining machine learning with traditional signal processing have been proposed [3] . For example, Short Time Fourier Transform [4,5] , wavelet transform [6,7] were used to extract frequency features combined with convolutional neural network (CNN) for fault diagnosis [8] . In [9], the multiscale entropy of the signal and extreme learning machine were applied to achieve the initial looseness detection of pipe clamps.…”

Section: Introductionmentioning

confidence: 99%

Looseness extent recognition of hydraulic pipeline clamps based on multi-scale feature fusion CNN model

Wang

Wei

Yang

2023

Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023)

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The clamp, as a fixed connection element of hydraulic pipeline system, its condition affects sustainability, reliability and safety of the whole system. Therefore, it is essential to identify level of loosed clamp caused by wear and tear. This paper proposes a clamp looseness extent recognition method based on multi-scale feature fusion convolutional neural network (MFF-CNN). The 2-D input constructed by small sample data of 7 distributed FBG sensors is useful for integration of temporal-spatial feature of clamp looseness. Furthermore, multi-scale fused features in MFF-CNN are more effective to overcome problem of small sample data caused by transient nature of mechanical faults and improves efficiency of CNN model training. The proposed model provides better performance on clamp looseness extent recognition compared to 1DCNN, 2DCNN and MSCNN. The average recognition accuracy reaches up to 96.3%.

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Ball screw fault diagnosis based on continuous wavelet transform and two-dimensional convolution neural network

Cited by 15 publications

References 14 publications

Measurement and reconstruction of geometric parameters of the barrel bore based on the laser scanning strategy

Measurement and reconstruction of geometric parameters of the barrel bore based on the laser scanning strategy

Fault diagnosis of rotating machinery using novel self-attention mechanism TCN with soft thresholding method

Looseness extent recognition of hydraulic pipeline clamps based on multi-scale feature fusion CNN model

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